3D ultrasound-based instrument for non-invasive measurement of amniotic fluid volume

ABSTRACT

A hand-held 3D ultrasound instrument is disclosed which is used to non-invasively and automatically measure amniotic fluid volume in the uterus requiring a minimum of operator intervention. Using a 2D image-processing algorithm, the instrument gives automatic feedback to the user about where to acquire the 3D image set. The user acquires one or more 3D data sets covering all of the amniotic fluid in the uterus and this data is then processed using an optimized 3D algorithm to output the total amniotic fluid volume corrected for any fetal head brain volume contributions.

PRIORITY CLAIM

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005claims priority to U.S. provisional patent application Ser. No.60/566,823 filed Apr. 30, 2004.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005claims priority to and is a continuation-in-part of U.S. patentapplication Ser. No. 10/701,955 filed Nov. 5, 2003, now U.S. Pat. No.7,087,022 which in turn claims priority to and is a continuation-in-partof U.S. patent application Ser. No. 10/443,126 filed May 20, 2003 nowU.S. Pat. No. 7,041,059.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 isa continuation-in-part of and claims priority to PCT application serialnumber PCT/US03/24368 filed Aug. 1, 2003, which claims priority to U.S.provisional patent application Ser. No. 60/423,881 filed Nov. 5, 2002and U.S. provisional patent application Ser. No. 60/400,624 filed Aug.2, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 isalso a continuation-in-part of and claims priority to PCT ApplicationSerial No. PCT/US03/14785 filed May 9, 2003, which is a continuation ofU.S. patent application Ser. No. 10/165,556 filed Jun. 7, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 isalso a continuation-in-part of and claims priority to U.S. patentapplication Ser. No. 10/633,186 filed Jul. 7, 2003 which claims priorityto U.S. provisional patent application Ser. No. 60/423,881 filed Nov. 5,2002 and U.S. provisional patent application Ser. No. 60/423,881 filedAug. 2, 2002, and to U.S. patent application Ser. No. 10/443,126 filedMay 20, 2003 which claims priority to U.S. provisional patentapplication Ser. No. 60/423,881 filed Nov. 5, 2002 and to U.S.provisional application 60/400,624 filed Aug. 2, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005also claims priority to U.S. provisional patent application Ser. No.60/470,525 filed May 12, 2003, and to U.S. patent application Ser. No.10/165,556 filed Jun. 7, 2002. All of the above applications are hereinincorporated by reference in their entirety as if fully set forthherein.

FIELD OF THE INVENTION

This invention pertains to the field of obstetrics, particularly toultrasound-based non-invasive obstetric measurements.

BACKGROUND OF THE INVENTION

Measurement of the amount of Amniotic Fluid (AF) volume is critical forassessing the kidney and lung function of a fetus and also for assessingthe placental function of the mother. Amniotic fluid volume is also akey measure to diagnose conditions such as polyhydramnios (too much AF)and oligohydramnios (too little AF). Polyhydramnios and oligohydramniosare diagnosed in about 7-8% of all pregnancies and these conditions areof concern because they may lead to birth defects or to deliverycomplications. The amniotic fluid volume is also one of the importantcomponents of the fetal biophysical profile, a major indicator of fetalwell-being.

The currently practiced and accepted method of quantitatively estimatingthe AF volume is from two-dimensional (2D) ultrasound images. The mostcommonly used measure is known as the use of the amniotic fluid index(AFI). AFI is the sum of vertical lengths of the largest AF pockets ineach of the 4 quadrants. The four quadrants are defined by the umbilicus(the navel) and the linea nigra (the vertical mid-line of the abdomen).The transducer head is placed on the maternal abdomen along thelongitudinal axis with the patient in the supine position. This measurewas first proposed by Phelan et al (Phelan J P, Smith C V, Broussard P,Small M., “Amniotic fluid volume assessment with the four-quadranttechnique at 36-42 weeks' gestation,” J Reprod Med Jul; 32(7): 540-2,1987) and then recorded for a large normal population over time by Mooreand Cayle (Moore T R, Cayle J E. “The amniotic fluid index in normalhuman pregnancy,” Am J Obstet Gynecol May; 162(5): 1168-73, 1990).

Even though the AFI measure is routinely used, studies have shown a verypoor correlation of the AFI with the true AF volume (Sepulveda W, FlackN J, Fisk N M., “Direct volume measurement at midtrimester amnioinfusionin relation to ultrasonographic indexes of amniotic fluid volume,” Am JObstet Gynecol Apr; 170(4): 1160-3, 1994). The correlation coefficientwas found to be as low as 0.55, even for experienced sonographers. Theuse of vertical diameter only and the use of only one pocket in eachquadrant are two reasons why the AFI is not a very good measure of AFVolume (AFV). Some of the other methods that have been used to estimateAF volume include:

Dye dilution technique. This is an invasive method where a dye isinjected into the AF during amniocentesis and the final concentration ofdye is measured from a sample of AF removed after several minutes. Thistechnique is the accepted gold standard for AF volume measurement;however, it is an invasive and cumbersome method and is not routinelyused.

Subjective interpretation from ultrasound images. This technique isobviously dependent on observer experience and has not been found to bevery good or consistent at diagnosing oligo- or poly-hydramnios.

Vertical length of the largest single cord-free pocket. This is anearlier variation of the AFI where the diameter of only one pocket ismeasured to estimate the AF volume.

Two-diameter areas of the largest AF pockets in the four quadrants. Thisis similar to the AFI; however, in this case, two diameters are measuredinstead of only one for the largest pocket. This two diameter area hasbeen recently shown to be better than AFI or the single pocketmeasurement in identifying oligohydramnios (Magann E F, Perry K G Jr,Chauhan S P, Anfanger P J, Whitworth N S, Morrison J C., “The accuracyof ultrasound evaluation of amniotic fluid volume in singletonpregnancies: the effect of operator experience and ultrasoundinterpretative technique,” J Clin Ultrasound, Jun; 25(5):249-53, 1997).

See also: U.S. Pat. No. 6,346,124 to Geiser, et al. (Autonomous BoundaryDetection System For Echocardiographic Images). Similarly, themeasurement of bladder structures are covered in U.S. Pat. No. 6,213,949to Ganguly, et al. (System For Estimating Bladder Volume) and U.S. Pat.No. 5,235,985 to McMorrow, et al., (Automatic Bladder ScanningApparatus). The measurement of fetal head structures is described inU.S. Pat. No. 5,605,155 to Chalana, et al., (Ultrasound System ForAutomatically Measuring Fetal Head Size). The measurement of fetalweight is described in U.S. Pat. No. 6,375,616 to Soferman, et al.(Automatic Fetal Weight Determination), Segiv et al. (in Segiv C,Akselrod S, Tepper R., “Application of a semiautomatic boundarydetection algorithm for the assessment of amniotic fluid quantity fromultrasound images”, Ultrasound Med Biol, May; 25(4): 515-26, 1999),Grover et al. (Grover J, Mentakis E A, Ross M G, “Three-dimensionalmethod for determination of amniotic fluid volume in intrauterinepockets.”Obstet Gynecol, Dec; 90(6): 1007-10, 1997). None of thecurrently used methods for AF volume estimation are ideal. Therefore,there is a need for better, non-invasive, and easier ways to accuratelymeasure amniotic fluid volume.

SUMMARY OF THE INVENTION

A preferred form of the invention utilizes a three dimensional (3D)ultrasound-based system and method preferably using a hand-held 3Dultrasound device to acquire at least one 3D data set of a uterus andhaving one or more, or preferably a plurality of automated processesoptimized to accurately and precisely locate and measure the volume ofamniotic fluid in the uterus without resorting to pre-supposed models ofthe shapes of amniotic fluid pockets in ultrasound images. The automatedprocess uses one or more, or preferably a plurality, of algorithms,preferably in a sequence that includes steps for image enhancement,segmentation, and polishing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a side view of a microprocessor-controlled, hand-heldultrasound transceiver;

FIG. 2A is a is depiction of the hand-held transceiver in use forscanning a patient;

FIG. 2B is a perspective view of the hand-held transceiver devicesitting in a communication cradle;

FIG. 2C is a perspective view of an amniotic fluid volume measuringsystem;

FIG. 3 is an alternate embodiment of an amniotic fluid volume measuringsystem in schematic view of a plurality of transceivers in connectionwith a server;

FIG. 4 is another alternate embodiment of an amniotic fluid volumemeasuring system in a schematic view of a plurality of transceivers inconnection with a server over a network;

FIG. 5A a graphical representation of a plurality of scan lines forminga single scan plane;

FIG. 5B is a graphical representation of a plurality of scanplanesforming a three-dimensional array having a substantially conic shape;

FIG. 5C is a graphical representation of a plurality of 3D distributedscanlines emanating from the transceiver forming a scancone;

FIG. 6 is a depiction of the hand-held transceiver placed laterally on apatient trans-abdominally to transmit ultrasound and receive ultrasoundechoes for processing to determine amniotic fluid volumes;

FIG. 7 shows a block diagram overview of the two-dimensional andthree-dimensional Input, Image Enhancement, Intensity-BasedSegmentation, Edge-Based Segmentation, Combine, Polish, Output, andCompute algorithms to visualize and determine the volume or area ofamniotic fluid;

FIG. 8A depicts the sub-algorithms of Image Enhancement;

FIG. 8B depicts the sub-algorithms of Intensity-Based Segmentation;

FIG. 8C depicts the sub-algorithms of Edge-Based Segmentation;

FIG. 8D depicts the sub-algorithms of the Polish algorithm, includingClose, Open, Remove Deep Regions, and Remove Fetal Head Regions;

FIG. 8E depicts the sub-algorithms of the Remove Fetal Head Regionssub-algorithm;

FIG. 8F depicts the sub-algorithms of the Hough Transform sub-algorithm;

FIG. 9 depicts the operation of a circular Hough transform algorithm;

FIG. 10 shows results of sequentially applying the algorithm steps on asample image;

FIG. 11 illustrates a set of intermediate images of the fetal headdetection process;

FIG. 12 presents a 4-panel series of sonographer amniotic fluid pocketoutlines and the algorithm output amniotic fluid pocket outlines;

FIG. 13 illustrates a 4-quadrant supine procedure to acquire multipleimage cones;

FIG. 14 illustrates an in-line lateral line procedure to acquiremultiple image cones;

FIG. 15 is a block diagram overview of the rigid registration andcorrecting algorithms used in processing multiple image cone data sets;

FIG. 16 is a block diagram of the steps in the rigid registrationalgorithm;

FIG. 17A is an example image showing a first view of a fixed scanplane;

FIG. 17B is an example image showing a second view view of a movingscanplane having some voxels in common with the first scanplane;

FIG. 17C is a composite image of the first (fixed) and second (moving)images;

FIG. 18A is an example image showing a first view of a fixed scanplane;

FIG. 18B is an example image showing a second view of a moving scanplanehaving some voxels in common with the first view and a third view;

FIG. 18C is a third view of a moving scanplane having some voxels incommon with the second view;

FIG. 18D is a composite image of the first (fixed), second (moving), andthird (moving) views;

FIG. 19 illustrates a 6-section supine procedure to acquire multipleimage cones around the center point of uterus of a patient in a supineprocedure;

FIG. 20 is a block diagram algorithm overview of the registration andcorrecting algorithms used in processing the 6-section multiple imagecone data sets depicted in FIG. 19;

FIG. 21 is an expansion of the Image Enhancement and Segmentation block1010 of FIG. 20;

FIG. 22 is an expansion of the RigidRegistration block 1014 of FIG. 20;

FIG. 23 is a 4-panel image set that shows the effect of multipleiterations of the heat filter applied to an original image;

FIG. 24 shows the affect of shock filtering and a combinationheat-and-shock filtering to the pixel values of the image;

FIG. 25 is a 7-panel image set progressively receiving application ofthe image enhancement and segmentation algorithms of FIG. 21;

FIG. 26 is a pixel difference kernel for obtaining X and Y derivativesto determine pixel gradient magnitudes for edge-based segmentation; and

FIG. 27 is a 3-panel image set showing the progressive demarcation oredge detection of organ wall interfaces arising from edge-basedsegmentation algorithms.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Preferably, a hand-held 3D ultrasound device is used to image the uterustrans-abdominally. The user moves the device around on the maternalabdomen and, using 2D image processing to locate the amniotic fluidareas, the device gives feedback to the user about where to acquire the3D image data sets. The user acquires one or more 3D image data setscovering all of the amniotic fluid in the uterus and the data sets arethen stored in the device or transferred to a host computer.

The 3D ultrasound device is configured to acquire the 3D image data setsin two formats. The first format is a collection of two-dimensionalscanplanes, each scanplane being separated from the other andrepresenting a portion of the uterus being scanned. Each scanplane isformed from one-dimensional ultrasound A-lines confined within thelimits of the 2D scanplane. The 3D data sets is then represented as a 3Darray of 2D scanplanes. The 3D array of 2D scanplanes is an assembly ofscanplanes, and may be assembled into a translational array, a wedgearray, or a rotatational array.

Alternatively, the 3D ultrasound device is configured to acquire the 3Dimage data sets from one-dimensional ultrasound A-lines distributed in3D space of the uterus to form a 3D scancone of 3D-distributed scanline.The 3D scancone is not an assembly of 2D scanplanes.

The 3D image datasets, either as discrete scanplanes or 3D distributedscanlines, are then subjected to image enhancement and analysisprocesses. The processes are either implemented on the device itself orare implemented on the host computer. Alternatively, the processes canalso be implemented on a server or other computer to which the 3Dultrasound data sets are transferred.

In a preferred image enhancement process, each 2D image in the 3Ddataset is first enhanced using non-linear filters by an imagepre-filtering step. The image pre-filtering step includes animage-smoothing step to reduce image noise followed by animage-sharpening step to obtain maximum contrast between organ wallboundaries.

A second process includes subjecting the resulting image of the firstprocess to a location method to identify initial edge points betweenamniotic fluid and other fetal or maternal structures. The locationmethod automatically determines the leading and trailing regions of walllocations along an A-mode one-dimensional scan line.

A third process includes subjecting the image of the first process to anintensity-based segmentation process where dark pixels (representingfluid) are automatically separated from bright pixels (representingtissue and other structures).

In a fourth process, the images resulting from the second and third stepare combined to result in a single image representing likely amnioticfluid regions.

In a fifth process, the combined image is cleaned to make the outputimage smooth and to remove extraneous structures such as the fetal headand the fetal bladder.

In a sixth process, boundary line contours are placed on each 2D image.Thereafter, the method then calculates the total 3D volume of amnioticfluid in the uterus.

In cases in which uteruses are too large to fit in a single 3D array of2D scanplanes or a single 3D scancone of 3D distributed scanlines,especially as occurs during the second and third trimester of pregnancy,preferred alternate embodiments of the invention allow for acquiring atleast two 3D data sets, preferably four, each 3D data set having atleast a partial ultrasonic view of the uterus, each partial viewobtained from a different anatomical site of the patient.

In one embodiment a 3D array of 2D scanplanes is assembled such that the3D array presents a composite image of the uterus that displays theamniotic fluid regions to provide the basis for calculation of amnioticfluid volumes. In a preferred alternate embodiment, the user acquiresthe 3D data sets in quarter sections of the uterus when the patient isin a supine position. In this 4-quadrant supine procedure, four imagecones of data are acquired near the midpoint of each uterine quadrant atsubstantially equally spaced intervals between quadrant centers. Imageprocessing as outlined above is conducted for each quadrant image,segmenting on the darker pixels or voxels associated with amnioticfluid. Correcting algorithms are applied to compensate for anyquadrant-to-quadrant image cone overlap by registering and fixing onequadrant's image to another. The result is a fixed 3D mosaic image ofthe uterus and the amniotic fluid volumes or regions in the uterus fromthe four separate image cones.

Similarly, in another preferred alternate embodiment, the user acquiresone or more 3D image data sets of quarter sections of the uterus whenthe patient is in a lateral position. In this multi-image cone lateralprocedure, each image's cones of data are acquired along a lateral lineof substantially equally spaced intervals. Each image cone is subjectedto the image processing as outlined above, with emphasis given tosegmenting on the darker pixels or voxels associated with amnioticfluid. Scanplanes showing common pixel or voxel overlaps are registeredinto a common coordinate system along the lateral line. Correctingalgorithms are applied to compensate for any image cone overlap alongthe lateral line. The result is a fixed 3D mosaic image of the uterusand the amniotic fluid volumes or regions in the uterus from the fourseparate image cones.

In yet other preferred embodiments, at least two 3D scancone of 3Ddistributed scanlines are acquired at different anatomical sites, imageprocessed, registered and fused into a 3D mosaic image composite.Amniotic fluid volumes are then calculated.

The system and method further provides an automatic method to detect andcorrect for any contribution the fetal head provides to the amnioticfluid volume.

The preferred portable embodiment of the ultrasound transceiver of theamniotic fluid volume measuring system are shown in FIGS. 1-4. Thetransceiver 10 includes a handle 12 having a trigger 14 and a top button16, a transceiver housing 18 attached to the handle 12, and atransceiver dome 20. A display 24 for user interaction is attached tothe transceiver housing 18 at an end opposite the transceiver dome 20.Housed within the transceiver 10 is a single element transducer (notshown) that converts ultrasound waves to electrical signals. Thetransceiver 10 is held in position against the body of a patient by auser for image acquisition and signal processing. In operation, thetransceiver 10 transmits a radio frequency ultrasound signal atsubstantially 3.7 MHz to the body and then receives a returning echosignal. To accommodate different patients having a variable range ofobesity, the transceiver 10 can be adjusted to transmit a range ofprobing ultrasound energy from approximately 2 MHz to approximately 10MHz radio frequencies.

The top button 16 selects for different acquisition volumes. Thetransceiver is controlled by a microprocessor and software associatedwith the microprocessor and a digital signal processor of a computersystem. As used in this invention, the term “computer system” broadlycomprises any microprocessor-based or other computer system capable ofexecuting operating instructions and manipulating data, and is notlimited to a traditional desktop or notebook computer. The display 24presents alphanumeric or graphic data indicating the proper or optimalpositioning of the transceiver 10 for initiating a series of scans. Thetransceiver 10 is configured to initiate the series of scans to obtainand present 3D images as either a 3D array of 2D scanplanes or as asingle 3D scancone of 3D distributed scanlines. A suitable transceiveris the DCD372 made by Diagnostic Ultrasound. In alternate embodiments,the two- or three-dimensional image of a scan plane may be presented inthe display 24.

Although the preferred ultrasound transceiver is described above, othertransceivers may also be used. For example, the transceiver need not bebattery-operated or otherwise portable, need not have a top-mounteddisplay 24, and may include many other features or differences. Thedisplay 24 may be a liquid crystal display (LCD), a light emitting diode(LED), a cathode ray tube (CRT), or any suitable display capable ofpresenting alphanumeric data or graphic images.

FIG. 2A is a photograph of the hand-held transceiver 10 for scanning apatient. The transceiver 10 is then positioned over the patient'sabdomen by a user holding the handle 12 to place the transceiver housing18 against the patient's abdomen. The top button 16 is centrally locatedon the handle 12. Once optimally positioned over the abdomen forscanning, the transceiver 10 transmits an ultrasound signal atsubstantially 3.7 MHz into the uterus. The transceiver 10 receives areturn ultrasound echo signal emanating from the uterus and presents iton the display 24.

FIG. 2B is a perspective view of the hand-held transceiver devicesitting in a communication cradle. The transceiver 10 sits in acommunication cradle 42 via the handle 12. This cradle can be connectedto a standard USB port of any personal computer, enabling all the dataon the device to be transferred to the computer and enabling newprograms to be transferred into the device from the computer.

FIG. 2C is a perspective view of an amniotic fluid volume measuringsystem 5A. The system 5A includes the transceiver 10 cradled in thecradle 42 that is in signal communication with a computer 52. Thetransceiver 10 sits in a communication cradle 42 via the handle 12. Thiscradle can be connected to a standard USB port of any personal computer52, enabling all the data on the transceiver 10 to be transferred to thecomputer for analysis and determination of amniotic fluid volume.

FIG. 3 depicts an alternate embodiment of an amniotic fluid volumemeasuring system 5B in a schematic view. The system 5B includes aplurality systems 5A in signal communication with a server 56. Asillustrated each transceiver 10 is in signal connection with the server56 through connections via a plurality of computers 52. FIG. 3, byexample, depicts each transceiver 10 being used to send probingultrasound radiation to a uterus of a patient and to subsequentlyretrieve ultrasound echoes returning from the uterus, convert theultrasound echoes into digital echo signals, store the digital echosignals, and process the digital echo signals by algorithms of theinvention. A user holds the transceiver 10 by the handle 12 to sendprobing ultrasound signals and to receive incoming ultrasound echoes.The transceiver 10 is placed in the communication cradle 42 that is insignal communication with a computer 52, and operates as an amnioticfluid volume measuring system. Two amniotic fluid volume-measuringsystems are depicted as representative though fewer or more systems maybe used. As used in this invention, a “server” can be any computersoftware or hardware that responds to requests or issues commands to orfrom a client. Likewise, the server may be accessible by one or moreclient computers via the Internet, or may be in communication over a LANor other network.

Preferred amniotic fluid volume measuring systems include thetransceiver 10 for acquiring data from a patient. The transceiver 10 isplaced in the cradle 52 to establish signal communication with thecomputer 52. Signal communication as illustrated is, in one embodiment,by a wired connection from the cradle 42 to the computer 52. Signalcommunication between the transceiver 10 and the computer 52 may also beby wireless means, for example, infrared signals or radio frequencysignals. The wireless means of signal communication may occur betweenthe cradle 42 and the computer 52, the transceiver 10 and the computer52, or the transceiver 10 and the cradle 42.

A preferred first embodiment of the amniotic fluid volume measuringsystem includes each transceiver 10 being separately used on a patientand sending signals proportionate to the received and acquiredultrasound echoes to the computer 52 for storage. Residing in eachcomputer 52 are imaging programs having instructions to prepare andanalyze a plurality of one dimensional (1D) images from the storedsignals and transforms the plurality of 1D images into the plurality of2D scanplanes. The imaging programs also present 3D renderings from theplurality of 2D scanplanes. Also residing in each computer 52 areinstructions to perform the additional ultrasound image enhancementprocedures, including instructions to implement the image processingalgorithms.

A preferred second embodiment of the amniotic fluid volume measuringsystem is similar to the first embodiment, but the imaging programs andthe instructions to perform the additional ultrasound enhancementprocedures are located on the server 56. Each computer 52 from eachamniotic fluid volume measuring system receives the acquired signalsfrom the transceiver 10 via the cradle 51 and stores the signals in thememory of the computer 52. The computer 52 subsequently retrieves theimaging programs and the instructions to perform the additionalultrasound enhancement procedures from the server 56. Thereafter, eachcomputer 52 prepares the 1D images, 2D images, 3D renderings, andenhanced images from the retrieved imaging and ultrasound enhancementprocedures. Results from the data analysis procedures are sent to theserver 56 for storage.

A preferred third embodiment of the amniotic fluid volume measuringsystem is similar to the first and second embodiments, but the imagingprograms and the instructions to perform the additional ultrasoundenhancement procedures are located on the server 56 and executed on theserver 56. Each computer 52 from each amniotic fluid volume measuringsystem receives the acquired signals from the transceiver 10 and via thecradle 51 sends the acquired signals in the memory of the computer 52.The computer 52 subsequently sends the stored signals to the server 56.In the server 56, the imaging programs and the instructions to performthe additional ultrasound enhancement procedures are executed to preparethe 1D images, 2D images, 3D renderings, and enhanced images from theserver 56 stored signals. Results from the data analysis procedures arekept on the server 56, or alternatively, sent to the computer 52.

FIG. 4 is another embodiment of an amniotic volume fluid measuringsystem 5C presented in schematic view. The system 5C includes aplurality of amniotic fluid measuring systems 5A connected to a server56 over the Internet or other network 64. FIG. 4 represents any of thefirst, second, or third embodiments of the invention advantageouslydeployed to other servers and computer systems through connections viathe network.

FIG. 5A a graphical representation of a plurality of scan lines forminga single scan plane. FIG. 5A illustrates how ultrasound signals are usedto make analyzable images, more specifically how a series ofone-dimensional (1D) scanlines are used to produce a two-dimensional(2D) image. The 1D and 2D operational aspects of the single elementtransducer housed in the transceiver 10 is seen as it rotatesmechanically about an angle φ. A scanline 214 of length r migratesbetween a first limiting position 218 and a second limiting position 222as determined by the value of the angle φ, creating a fan-like 2Dscanplane 210. In one preferred form, the transceiver 10 operatessubstantially at 3.7 MHz frequency and creates an approximately 18 cmdeep scan line 214 and migrates within the angle φ having an angle ofapproximately 0.027 radians. A first motor tilts the transducerapproximately 60° clockwise and then counterclockwise forming thefan-like 2D scanplane presenting an approximate 120° 2D sector image. Aplurality of scanlines, each scanline substantially equivalent toscanline 214 is recorded, between the first limiting position 218 andthe second limiting position 222 formed by the unique tilt angle φ. Theplurality of scanlines between the two extremes forms a scanplane 210.In the preferred embodiment, each scanplane contains 77 scan lines,although the number of lines can vary within the scope of thisinvention. The tilt angle φ sweeps through angles approximately between−60° and +60° for a total arc of approximately 120°.

FIG. 5B is a graphical representation of a plurality of scanplanesforming a three-dimensional array (3D) 240 having a substantially conicshape. FIG. 5B illustrates how a 3D rendering is obtained from theplurality of 2D scanplanes. Within each scanplane 210 are the pluralityof scanlines, each scanline equivalent to the scanline 214 and sharing acommon rotational angle θ. In the preferred embodiment, each scanplanecontains 77 scan lines, although the number of lines can vary within thescope of this invention. Each 2D sector image scanplane 210 with tiltangle φ and range r (equivalent to the scanline 214) collectively formsa 3D conic array 240 with rotation angle θ. After gathering the 2Dsector image, a second motor rotates the transducer between 3.75° or7.5° to gather the next 120° sector image. This process is repeateduntil the transducer is rotated through 180°, resulting in thecone-shaped 3D conic array 240 data set with 24 planes rotationallyassembled in the preferred embodiment. The conic array could have feweror more planes rotationally assembled. For example, preferred alternateembodiments of the conic array could include at least two scanplanes, ora range of scanplanes from 2 to 48 scanplanes. The upper range of thescanplanes can be greater than 48 scanplanes. The tilt angle φ indicatesthe tilt of the scanline from the centerline in 2D sector image, and therotation angle θ, identifies the particular rotation plane the sectorimage lies in. Therefore, any point in this 3D data set can be isolatedusing coordinates expressed as three parameters, P(r, φ, θ).

As the scanlines are transmitted and received, the returning echoes areinterpreted as analog electrical signals by a transducer, converted todigital signals by an analog-to-digital converter, and conveyed to thedigital signal processor of the computer system for storage and analysisto determine the locations of the amniotic fluid walls. The computersystem is representationally depicted in FIGS. 3 and 4 and includes amicroprocessor, random access memory (RAM), or other memory for storingprocessing instructions and data generated by the transceiver 10.

FIG. 5C is a graphical representation of a plurality of 3D-distributedscanlines emanating from the transceiver 10 forming a scancone 300. Thescancone 300 is formed by a plurality of 3D distributed scanlines thatcomprises a plurality of internal and peripheral scanlines. Thescanlines are one-dimensional ultrasound A-lines that emanate from thetransceiver 10 at different coordinate directions, that taken as anaggregate, from a conic shape. The 3D-distributed A-lines (scanlines)are not necessarily confined within a scanplane, but instead aredirected to sweep throughout the internal and along the periphery of thescancone 300. The 3D-distributed scanlines not only would occupy a givenscanplane in a 3D array of 2D scanplanes, but also the inter-scanplanespaces, from the conic axis to and including the conic periphery. Thetransceiver 10 shows the same illustrated features from FIG. 1, but isconfigured to distribute the ultrasound A-lines throughout 3D space indifferent coordinate directions to form the scancone 300.

The internal scanlines are represented by scanlines 312A-C. The numberand location of the internal scanlines emanating from the transceiver 10is the number of internal scanlines needed to be distributed within thescancone 300, at different positional coordinates, to sufficientlyvisualize structures or images within the scancone 300. The internalscanlines are not peripheral scanlines. The peripheral scanlines arerepresented by scanlines 314A-F and occupy the conic periphery, thusrepresenting the peripheral limits of the scancone 300.

FIG. 6 is a depiction of the hand-held transceiver placed on a patienttrans-abdominally to transmit probing ultrasound and receive ultrasoundechoes for processing to determine amniotic fluid volumes. Thetransceiver 10 is held by the handle 12 to position over a patient tomeasure the volume of amniotic fluid in an amniotic sac over a baby. Aplurality of axes for describing the orientation of the baby, theamniotic sac, and mother is illustrated. The plurality of axes includesa vertical axis depicted on the line L (R)-L(L) for left and rightorientations, a horizontal axis LI-LS for inferior and superiororientations, and a depth axis LA-LP for anterior and posteriororientations.

FIG. 6 is representative of a preferred data acquisition protocol usedfor amniotic fluid volume determination. In this protocol, thetransceiver 10 is the hand-held 3D ultrasound device (for example, modelDCD372 from Diagnostic Ultrasound) and is used to image the uterustrans-abdominally. Initially during the targeting phase, the patient isin a supine position and the device is operated in a 2D continuousacquisition mode. A 2D continuous mode is where the data is continuouslyacquired in 2D and presented as a scanplane similar to the scanplane 210on the display 24 while an operator physically moves the transceiver 10.An operator moves the transceiver 10 around on the maternal abdomen andthe presses the trigger 14 of the transceiver 10 and continuouslyacquires real-time feedback presented in 2D on the display 24. Amnioticfluid, where present, visually appears as dark regions along with analphanumeric indication of amniotic fluid area (for example, in cm²) onthe display 24. Based on this real-time information in terms of therelative position of the transceiver 10 to the fetus, the operatordecides which side of the uterus has more amniotic fluid by thepresentation on the display 24. The side having more amniotic fluidpresents as regions having larger darker regions on the display 24.Accordingly, the side displaying a large dark region registers greateralphanumeric area while the side with less fluid shows displays smallerdark regions and proportionately registers smaller alphanumeric area onthe display 24. While amniotic fluid is present throughout the uterus,its distribution in the uterus depends upon where and how the fetus ispositioned within the uterus. There is usually less amniotic fluidaround the fetus's spine and back and more amniotic fluid in front ofits abdomen and around the limbs.

Based on fetal position information acquired from data gathered undercontinuous acquisition mode, the patient is placed in a lateralrecumbent position such that the fetus is displaced towards the groundcreating a large pocket of amniotic fluid close to abdominal surfacewhere the transceiver 10 can be placed as shown in FIG. 6. For example,if large fluid pockets are found on the right side of the patient, thepatient is asked to turn with the left side down and if large fluidpockets are found on the left side, the patient is asked to turn withthe right side down.

After the patient has been placed in the desired position, thetransceiver 10 is again operated in the 2D continuous acquisition modeand is moved around on the lateral surface of the patient's abdomen. Theoperator finds the location that shows the largest amniotic fluid areabased on acquiring the largest dark region imaged and the largestalphanumeric value displayed on the display 24. At the lateral abdominallocation providing the largest dark region, the transceiver 10 is heldin a fixed position, the trigger 14 is released to acquire a 3D imagecomprising a set of arrayed scanplanes. The 3D image presents arotational array of the scanplanes 210 similar to the 3D array 240.

In a preferred alternate data acquisition protocol, the operator canreposition the transceiver 10 to different abdominal locations toacquire new 3D images comprised of different scanplane arrays similar tothe 3D array 240. Multiple scan cones obtained from different positionsprovide the operator the ability to image the entire amniotic fluidregion from different view points. In the case of a single image conebeing too small to accommodate a large AFV measurement, obtainingmultiple 3D array 240 image cones ensures that the total volume of largeAFV regions is determined. Multiple 3D images may also be acquired bypressing the top bottom 16 to select multiple conic arrays similar tothe 3D array 240.

Depending on the position of the fetus relative to the location of thetransceiver 10, a single image scan may present an underestimated volumeof AFV due to amniotic fluid pockets that remain hidden behind the limbsof the fetus. The hidden amniotic fluid pockets present asunquantifiable shadow-regions.

To guard against underestimating AFV, repeated positioning thetransceiver 10 and rescanning can be done to obtain more than oneultrasound view to maximize detection of amniotic fluid pockets.Repositioning and rescanning provides multiple views as a plurality ofthe 3D arrays 240 images cones. Acquiring multiple images cones improvesthe probability of obtaining initial estimates of AFV that otherwisecould remain undetected and un-quantified in a single scan.

In an alternative scan protocol, the user determines and scans at onlyone location on the entire abdomen that shows the maximum amniotic fluidarea while the patient is the supine position. As before, when the userpresses the top button 16, 2D scanplane images equivalent to thescanplane 210 are continuously acquired and the amniotic fluid area onevery image is automatically computed. The user selects one locationthat shows the maximum amniotic fluid area. At this location, as theuser releases the scan button, a full 3D data cone is acquired andstored in the device's memory.

FIG. 7 shows a block diagram overview the image enhancement,segmentation, and polishing algorithms of the amniotic fluid volumemeasuring system. The enhancement, segmentation, and polishingalgorithms are applied to each scanplane 210 or to the entire scan cone240 to automatically obtain amniotic fluid regions. For scanplanessubstantially equivalent to scanplane 210, the algorithms are expressedin two-dimensional terms and use formulas to convert scanplane pixels(picture elements) into area units. For the scan cones substantiallyequivalent to the 3D conic array 240, the algorithms are expressed inthree-dimensional terms and use formulas to convert voxels (volumeelements) into volume units.

The algorithms expressed in 2D terms are used during the targeting phasewhere the operator trans-abdominally positions and repositions thetransceiver 10 to obtain real-time feedback about the amniotic fluidarea in each scanplane. The algorithms expressed in 3D terms are used toobtain the total amniotic fluid volume computed from the voxelscontained within the calculated amniotic fluid regions in the 3D conicarray 240.

FIG. 7 represents an overview of a preferred method of the invention andincludes a sequence of algorithms, many of which have sub-algorithmsdescribed in more specific detail in FIGS. 8A-F. FIG. 7 begins withinputting data of an unprocessed image at step 410. After unprocessedimage data 410 is entered (e.g., read from memory, scanned, or otherwiseacquired), it is automatically subjected to an image enhancementalgorithm 418 that reduces the noise in the data (including specklenoise) using one or more equations while preserving the salient edges onthe image using one or more additional equations. Next, the enhancedimages are segmented by two different methods whose results areeventually combined. A first segmentation method applies anintensity-based segmentation algorithm 422 that determines all pixelsthat are potentially fluid pixels based on their intensities. A secondsegmentation method applies an edge-based segmentation algorithm 438that relies on detecting the fluid and tissue interfaces. The imagesobtained by the first segmentation algorithm 422 and the images obtainedby the second segmentation algorithm 438 are brought together via acombination algorithm 442 to provide a substantially segmented image.The segmented image obtained from the combination algorithm 442 are thensubjected to a polishing algorithm 464 in which the segmented image iscleaned-up by filling gaps with pixels and removing unlikely regions.The image obtained from the polishing algorithm 464 is outputted 480 forcalculation of areas and volumes of segmented regions-of-interest.Finally the area or the volume of the segmented region-of-interest iscomputed 484 by multiplying pixels by a first resolution factor toobtain area, or voxels by a second resolution factor to obtain volume.For example, for pixels having a size of 0.8 mm by 0.8 mm, the firstresolution or conversion factor for pixel area is equivalent to 0.64mm², and the second resolution or conversion factor for voxel volume isequivalent to 0.512 mm³. Different unit lengths for pixels and voxelsmay be assigned, with a proportional change in pixel area and voxelvolume conversion factors.

The enhancement, segmentation and polishing algorithms depicted in FIG.7 for measuring amniotic fluid areas or volumes are not limited toscanplanes assembled into rotational arrays equivalent to the 3D array240. As additional examples, the enhancement, segmentation and polishingalgorithms depicted in FIG. 7 apply to translation arrays and wedgearrays. Translation arrays are substantially rectilinear image planeslices from incrementally repositioned ultrasound transceivers that areconfigured to acquire ultrasound rectilinear scanplanes separated byregular or irregular rectilinear spaces. The translation arrays can bemade from transceivers configured to advance incrementally, or may behand-positioned incrementally by an operator. The operator obtains awedge array from ultrasound transceivers configured to acquirewedge-shaped scanplanes separated by regular or irregular angularspaces, and either mechanistically advanced or hand-tiltedincrementally. Any number of scanplanes can be either translationallyassembled or wedge-assembled ranges, but preferably in ranges greaterthan 2 scanplanes.

Other preferred embodiments of the enhancement, segmentation andpolishing algorithms depicted in FIG. 7 may be applied to images formedby line arrays, either spiral distributed or reconstructed random-lines.The line arrays are defined using points identified by the coordinatesexpressed by the three parameters, P(r, φ, θ), where the values or r, φ,and θ can vary.

The enhancement, segmentation and polishing algorithms depicted in FIG.7 are not limited to ultrasound applications but may be employed inother imaging technologies utilizing scanplane arrays or individualscanplanes. For example, biological-based and non-biological-basedimages acquired using infrared, visible light, ultraviolet light,microwave, x-ray computed tomography, magnetic resonance, gamma rays,and positron emission are images suitable for the algorithms depicted inFIG. 7. Furthermore, the algorithms depicted in FIG. 7 can be applied tofacsimile transmitted images and documents.

FIGS. 8A-E depict expanded details of the preferred embodiments ofenhancement, segmentation, and polishing algorithms described in FIG. 7.Each of the following greater detailed algorithms are either implementedon the transceiver 10 itself or are implemented on the host computer 52or on the server 56 computer to which the ultrasound data istransferred.

FIG. 8A depicts the sub-algorithms of Image Enhancement. Thesub-algorithms include a heat filter 514 to reduce noise and a shockfilter 518 to sharpen edges. A combination of the heat and shock filtersworks very well at reducing noise and sharpening the data whilepreserving the significant discontinuities. First, the noisy signal isfiltered using a 1D heat filter (Equation E1 below), which results inthe reduction of noise and smoothing of edges. This step is followed bya shock-filtering step 518 (Equation E2 below), which results in thesharpening of the blurred signal. Noise reduction and edge sharpening isachieved by application of the following equations E1-E2. The algorithmof the heat filter 514 uses a heat equation E1. The heat equation E1 inpartial differential equation (PDE) form for image processing isexpressed as:

${\frac{\partial u}{\partial t} = {\frac{\partial^{2}u}{\partial x^{2}} + \frac{\partial^{2}u}{\partial y^{2}}}},$

where u is the image being processed. The image u is 2D, and iscomprised of an array of pixels arranged in rows along the x-axis, andan array of pixels arranged in columns along the y-axis. The pixelintensity of each pixel in the image u has an initial input image pixelintensity (I) defined as u₀=I. The value of I depends on theapplication, and commonly occurs within ranges consistent with theapplication. For example, I can be as low as 0 to 1, or occupy middleranges between 0 to 127 or 0 to 512. Similarly, I may have valuesoccupying higher ranges of 0 to 1024 and 0 to 4096, or greater. The heatequation E1 results in a smoothing of the image and is equivalent to theGaussian filtering of the image. The larger the number of iterationsthat it is applied for the more the input image is smoothed or blurredand the more the noise that is reduced.

The shock filter 518 is a PDE used to sharpen images as detailed below.The two dimensional shock filter E2 is expressed as:

$\begin{matrix}{{\frac{\partial u}{\partial t} = {{- {F\left( {l(u)} \right)}}{{\nabla u}}}},} & {E2}\end{matrix}$where u is the image processed whose initial value is the input imagepixel intensity (I): u₀=I where the l(u) term is the Laplacian of theimage u, F is a function of the Laplacian, and ∥∇u∥ is the 2D gradientmagnitude of image intensity defined by equation E3.∥∇u∥=√{square root over (u _(x) ² +u _(y) ²)},  E3

-   -   where        -   u_(x) ²=the square of the partial derivative of the pixel            intensity (u) along the x-axis,        -   u_(y) ²=the square of the partial derivative of the pixel            intensity (u) along the y-axis,        -   the Laplacian l(u) of the image, u, is expressed in equation            E4 as            l(u)=u _(xx) u _(x) ²+2u _(xy) u _(x) u _(y) +u _(yy) u _(y)            ²  E4    -   where equation E4 relates to equation E1 as follows:        -   u_(x) is the first partial derivative

$\frac{\partial u}{\partial x}$of u along the x-axis,

-   -   -   u_(y) is the first partial derivative

$\frac{\partial u}{\partial y}$of u along the y-axis,

-   -   -   u_(x) ² is the square of the first partial derivative

$\frac{\partial u}{\partial x}$of u along the x-axis,

-   -   -   u_(y) ² is the square of the first partial derivative

$\frac{\partial u}{\partial y}$of u along the y-axis,

-   -   -   u_(xx) is the second partial derivative

$\frac{\partial^{2}u}{\partial x^{2}}$of u along the x-axis,

-   -   -   u_(yy) is the second partial derivative

$\frac{\partial^{2}u}{\partial y^{2}}$of u along the y-axis,

-   -   -   u_(xy) is cross multiple first partial derivative

$\frac{\partial u}{\partial{xdy}}$of u along the x and y axes, and

-   -   the sign of the function F modifies the Laplacian by the image        gradient values selected to avoid placing spurious edges at        points with small gradient values:

$\begin{matrix}{{{F\left( {l(u)} \right)} = 1},} & {{{if}\mspace{14mu}{l(u)}} > {0\mspace{14mu}{and}\mspace{14mu}{{\nabla u}}} > t} \\{{= {- 1}},} & {{{if}\mspace{14mu}{l(u)}} < {0\mspace{14mu}{and}\mspace{14mu}{{\nabla u}}} > t} \\{{= 0},} & {otherwise}\end{matrix}$

-   -   where t is a threshold on the pixel gradient value ∥∇u∥.

The combination of heat filtering and shock filtering produces anenhanced image ready to undergo the intensity-based and edge-basedsegmentation algorithms as discussed below.

FIG. 8B depicts the sub-algorithms of Intensity-Based Segmentation (step422 in FIG. 7). The intensity-based segmentation step 422 uses a“k-means” intensity clustering 522 technique where the enhanced image issubjected to a categorizing “k-means” clustering algorithm. The“k-means” algorithm categorizes pixel intensities into white, gray, andblack pixel groups. Given the number of desired clusters or groups ofintensities (k), the k-means algorithm is an iterative algorithmcomprising four steps:

1. Initially determine or categorize cluster boundaries by defining aminimum and a maximum pixel intensity value for every white, gray, orblack pixels into groups or k-clusters that are equally spaced in theentire intensity range.

2. Assign each pixel to one of the white, gray or black k-clusters basedon the currently set cluster boundaries.

3. Calculate a mean intensity for each pixel intensity k-cluster orgroup based on the current assignment of pixels into the differentk-clusters. The calculated mean intensity is defined as a clustercenter. Thereafter, new cluster boundaries are determined as mid pointsbetween cluster centers.

4. Determine if the cluster boundaries significantly change locationsfrom their previous values. Should the cluster boundaries changesignificantly from their previous values, iterate back to step 2, untilthe cluster centers do not change significantly between iterations.Visually, the clustering process is manifest by the segmented image andrepeated iterations continue until the segmented image does not changebetween the iterations.

The pixels in the cluster having the lowest intensity value—the darkestcluster—are defined as pixels associated with amniotic fluid. For the 2Dalgorithm, each image is clustered independently of the neighboringimages. For the 3D algorithm, the entire volume is clustered together.To make this step faster, pixels are sampled at 2 or any multiplesampling rate factors before determining the cluster boundaries. Thecluster boundaries determined from the down-sampled data are thenapplied to the entire data.

FIG. 8C depicts the sub-algorithms of Edge-Based Segmentation (step 438in FIG. 7) and uses a sequence of four sub-algorithms. The sequenceincludes a spatial gradients 526 algorithm, a hysteresis threshold 530algorithm, a Region-of-Interest (ROI) 534 algorithm, and a matchingedges filter 538 algorithm.

The spatial gradient 526 computes the x-directional and y-directionalspatial gradients of the enhanced image. The Hysteresis threshold 530algorithm detects salient edges. Once the edges are detected, theregions defined by the edges are selected by a user employing the ROI534 algorithm to select regions-of-interest deemed relevant foranalysis.

Since the enhanced image has very sharp transitions, the edge points canbe easily determined by taking x- and y-derivatives using backwarddifferences along x- and y-directions. The pixel gradient magnitude ∥∇I∥is then computed from the x- and y-derivative image in equation E5 as:∥∇I∥=√{square root over (I _(x) ² +I _(y) ²)}  E5

Where I_(x) ²=the square of x-derivative of intensity; and

-   -   I_(y) ²=the square of y-derivative of intensity along the        y-axis.

Significant edge points are then determined by thresholding the gradientmagnitudes using a hysteresis thresholding operation. Other thresholdingmethods could also be used. In hysteresis thresholding 530, twothreshold values, a lower threshold and a higher threshold, are used.First, the image is thresholded at the lower threshold value and aconnected component labeling is carried out on the resulting image.Next, each connected edge component is preserved which has at least oneedge pixel having a gradient magnitude greater than the upper threshold.This kind of thresholding scheme is good at retaining long connectededges that have one or more high gradient points.

In the preferred embodiment, the two thresholds are automaticallyestimated. The upper gradient threshold is estimated at a value suchthat at most 97% of the image pixels are marked as non-edges. The lowerthreshold is set at 50% of the value of the upper threshold. Thesepercentages could be different in different implementations. Next, edgepoints that lie within a desired region-of-interest are selected 534.This region of interest selection 534 excludes points lying at the imageboundaries and points lying too close to or too far from the transceiver10. Finally, the matching edge filter 538 is applied to remove outlieredge points and fill in the area between the matching edge points.

The edge-matching algorithm 538 is applied to establish valid boundaryedges and remove spurious edges while filling the regions betweenboundary edges. Edge points on an image have a directional componentindicating the direction of the gradient. Pixels in scanlines crossing aboundary edge location will exhibit two gradient transitions dependingon the pixel intensity directionality. Each gradient transition is givena positive or negative value depending on the pixel intensitydirectionality. For example, if the scanline approaches an echoreflective bright wall from a darker region, then an ascendingtransition is established as the pixel intensity gradient increases to amaximum value, i.e., as the transition ascends from a dark region to abright region. The ascending transition is given a positive numericalvalue. Similarly, as the scanline recedes from the echo reflective wall,a descending transition is established as the pixel intensity gradientdecreases to or approaches a minimum value. The descending transition isgiven a negative numerical value.

Valid boundary edges are those that exhibit ascending and descendingpixel intensity gradients, or equivalently, exhibit paired or matchedpositive and negative numerical values. The valid boundary edges areretained in the image. Spurious or invalid boundary edges do not exhibitpaired ascending-descending pixel intensity gradients, i.e., do notexhibit paired or matched positive and negative numerical values. Thespurious boundary edges are removed from the image.

For amniotic fluid volume related applications, most edge points foramniotic fluid surround a dark, closed region, with directions pointinginwards towards the center of the region. Thus, for a convex-shapedregion, the direction of a gradient for any edge point, the edge pointhaving a gradient direction approximately opposite to the current pointrepresents the matching edge point. Those edge points exhibiting anassigned positive and negative value are kept as valid edge points onthe image because the negative value is paired with its positive valuecounterpart. Similarly, those edge point candidates having unmatchedvalues, i.e., those edge point candidates not having a negative-positivevalue pair, are deemed not to be true or valid edge points and arediscarded from the image.

The matching edge point algorithm 538 delineates edge points not lyingon the boundary for removal from the desired dark regions. Thereafter,the region between any two matching edge points is filled in withnon-zero pixels to establish edge-based segmentation. In a preferredembodiment of the invention, only edge points whose directions areprimarily oriented co-linearly with the scanline are sought to permitthe detection of matching front wall and back wall pairs.

Returning to FIG. 7, once Intensity-Based 422 and Edge-BasedSegmentation 438 is completed, both segmentation methods use a combiningstep that combines the results of intensity-based segmentation 422 stepand the edge-based segmentation 438 step using an AND Operator of Images442. The AND Operator of Images 442 is achieved by a pixel-wise BooleanAND operator 442 step to produce a segmented image by computing thepixel intersection of two images. The Boolean AND operation 442represents the pixels as binary numbers and the corresponding assignmentof an assigned intersection value as a binary number 1 or 0 by thecombination of any two pixels. For example, consider any two pixels, saypixel_(A) and pixel_(B), which can have a 1 or 0 as assigned values. Ifpixel_(A)'s value is 1, and pixel_(B)'s value is 1, the assignedintersection value of pixel_(A) and pixel_(B) is 1. If the binary valueof pixel_(A) and pixel_(B) are both 0, or if either pixel_(A) orpixel_(B) is 0, then the assigned intersection value of pixel_(A) andpixel_(B) is 0. The Boolean AND operation 542 takes the binary any twodigital images as input, and outputs a third image with the pixel valuesmade equivalent to the intersection of the two input images.

Upon completion of the AND Operator of Images 442 algorithm, the polish464 algorithm of FIG. 7 is comprised of multiple sub-algorithms. FIG. 8Ddepicts the sub-algorithms of the Polish 464 algorithm, including aClose 546 algorithm, an Open 550 algorithm, a Remove Deep Regions 554algorithm, and a Remove Fetal Head Regions 560 algorithm.

Closing and opening algorithms are operations that process images basedon the knowledge of the shape of objects contained on a black and whiteimage, where white represents foreground regions and black representsbackground regions. Closing serves to remove background features on theimage that are smaller than a specified size. Opening serves to removeforeground features on the image that are smaller than a specified size.The size of the features to be removed is specified as an input to theseoperations. The opening algorithm 550 removes unlikely amniotic fluidregions from the segmented image based on a-priori knowledge of the sizeand location of amniotic fluid pockets.

Referring to FIG. 8D, the closing 546 algorithm obtains the ApparentAmniotic Fluid Area (AAFA) or Volume (AAFV) values. The AAFA and AAFVvalues are “Apparent” and maximal because these values may containregion areas or region volumes of non-amniotic origin unknowinglycontributing to and obscuring what otherwise would be the true amnioticfluid volume. For example, the AAFA and AAFV values contain the trueamniotic volumes, and possibly as well areas or volumes due to deeptissues and undetected fetal head volumes. Thus the apparent area andvolume values require correction or adjustments due to unknowncontributions of deep tissue and of the fetal head in order to determinean Adjusted Amniotic Fluid Area (AdAFA) value or Volume (AdAVA) value568.

The AdAFA and AdAVA values obtained by the Close 546 algorithm arereduced by the morphological opening algorithm 550. Thereafter, theAdAFA and AdAVA values are further reduced by removing areas and volumesattributable to deep regions by using the Remove Deep Regions 554algorithm. Thereafter, the polishing algorithm 464 continues by applyinga fetal head region detection algorithm 560.

FIG. 8E depicts the sub-algorithms of the Remove Fetal Head Regionssub-algorithm 560. The basic idea of the sub-algorithms of the fetalhead detection algorithm 560 is that the edge points that potentiallyrepresent a fetal skull are detected. Thereafter, a circle findingalgorithm to determine the best-fitting circle to these fetal skulledges is implemented. The radii of the circles that are searched areknown a priori based on the fetus' gestational age. The best fittingcircle whose fitting metric lies above a certain pre-specified thresholdis marked as the fetal head and the region inside this circle is thefetal head region. The algorithms include a gestational Age 726 input, adetermine head diameter factor 730 algorithm, a Head Edge Detectionalgorithm, 734, and a Hough transform procedure 736.

Fetal brain tissue has substantially similar ultrasound echo qualitiesas presented by amniotic fluid. If not detected and subtracted fromamniotic fluid volumes, fetal brain tissue volumes will be measured aspart of the total amniotic fluid volumes and lead to an overestimationand false diagnosis of oligo or poly-hyraminotic conditions. Thusdetecting fetal head position, measuring fetal brain matter volumes, anddeducting the fetal brain matter volumes from the amniotic fluid volumesto obtain a corrected amniotic fluid volume serves to establishaccurately measure amniotic fluid volumes.

The gestational age input 726 begins the fetal head detection algorithm560 and uses a head dimension table to obtain ranges of head bi-parietaldiameters (BPD) to search for (e.g., 30 week gestational age correspondsto a 6 cm head diameter). The head diameter range is input to both theHead Edge Detection, 734, and the Hough Transform, 736., The head edgedetection 734 algorithm seeks out the distinctively bright ultrasoundechoes from the anterior and posterior walls of the fetal skull whilethe Hough Transform algorithm, 736, finds the fetal head using circularshapes as models for the fetal head in the Cartesian image (pre-scanconversion to polar form).

Scanplanes processed by steps 522, 538, 530, are input to the head edgedetection step 734. Applied as the first step in the fetal headdetection algorithm 734 is the detection of the potential head edgesfrom among the edges found by the matching edge filter. The matchingedge 538 filter outputs pairs of edge points potentially belonging tofront walls or back walls. Not all of these walls correspond to fetalhead locations. The edge points representing the fetal head aredetermined using the following heuristics:

-   -   (1) Looking along a one dimensional A-mode scan line, fetal head        locations present a corresponding matching gradient in the        opposing direction within a short distance approximately the        same size as the thickness of the fetal skull. This distance is        currently set to a value 1 cm.    -   (2) The front wall and the back wall locations of the fetal head        are within a range of diameters corresponding to the expected        diameter 730 for the gestational age 726 of the fetus. Walls        that are too close or too far are not likely to be head        locations.    -   (3) A majority of the pixels between the front and back wall        locations of the fetal head lie within the minimum intensity        cluster as defined by the output of the clustering algorithm        422. The percentage of pixels that need to be dark is currently        defined to be 80%.

The pixels found satisfying these features are then vertically dilatedto produce a set of thick fetal head edges as the output of Head EdgeDetection, 734.

FIG. 8F depicts the sub-algorithms of the Hough transform procedure 736.The sub-algorithms include a Polar Hough Transform 738 algorithm, a findmaximum Hough value 742 algorithm 742, and a fill circle region 746. ThePolar Hough Transform algorithm looks for fetal head structures in polarcoordinate terms by converting from Cartesian coordinates using aplurality of equations. The fetal head, which appears like a circle in a3D scan-converted Cartesian coordinate image, has a different shape inthe pre-scan converted polar space. The fetal head shape is expressed interms of polar coordinate terms explained as follows:

The coordinates of a circle in the Cartesian space (x,y) with center(x₀, y₀) and radius R are defined for an angle θ are derived and definedin equation E5 as:x=R cos θ+x ₀y=R sin θ+y ₀

(x−x ₀)²+(y−y ₀)² =R ²  E5

In polar space, the coordinates (r,φ), with respect to the center(r₀,φ₀), are derived and defined in equation E6 as:r sin φ=R cos θ+r ₀ sin φ₀r cos φ=R sin θ+r ₀ cos φ⁰

(r sin φ−r ₀ sin φ₀)²+(r cos φ−r ₀ cos φ₀)² =R ²  E6

The Hough transform 736 algorithm using equations E5 and E6 attempts tofind the best-fit circle to the edges of an image. A circle in the polarspace is defined by a set of three parameters, (r₀, φ₀, R) representingthe center and the radius of the circle.

The basic idea for the Hough transform 736 is as follows. Suppose acircle is sought having a fixed radius (say, R1) for which the bestcenter of the circle is similarly sought. Now, every edge point on theinput image lies on a potential circle whose center lays R1 pixels awayfrom it. The set of potential centers themselves form a circle of radiusR1 around each edge pixel. Now, drawing potential circles of radius R1around each edge pixel, the point at which most circles intersect, acenter of the circle that represents a best-fit circle to the given edgepoints is obtained. Therefore, each pixel in the Hough transform outputcontains a likelihood value that is simply the count of the number ofcircles passing through that point.

FIG. 9 illustrates the Hough Transform 736 algorithm for a plurality ofcircles with a fixed radius in a Cartesian coordinate system. A portionof the plurality of circles is represented by a first circle 804 a, asecond circle 804 b, and a third circle 804 c. A plurality of edgepixels are represented as gray squares and an edge pixel 808 is shown. Acircle is drawn around each edge pixel to distinguish a center location812 of a best-fit circle 816 passing through each edge pixel point; thepoint of the center location through which most such circles pass (shownby a gray star 812) is the center of the best-fit circle 816 presentedas a thick dark line. The circumference of the best fit circle 816passes substantially through is central portion of each edge pixel,represented as a series of squares substantially equivalent to the edgepixel 808.

This search for best fitting circles can be easily extended to circleswith varying radii by adding one more degree of freedom—however, adiscrete set of radii around the mean radii for a given gestational agemakes the search significantly faster, as it is not necessary to searchall possible radii.

The next step in the head detection algorithm is selecting or rejectingbest-fit circles based on its likelihood, in the find maximum HoughValue 742 algorithm. The greater the number of circles passing through agiven point in the Hough-space, the more likely it is to be the centerof a best-fit circle. A 2D metric as a maximum Hough value 742 of theHough transform 736 output is defined for every image in a dataset. The3D metric is defined as the maximum of the 2D metrics for the entire 3Ddataset. A fetal head is selected on an image depending on whether its3D metric value exceeds a preset 3D threshold and also whether the 2Dmetric exceeds a preset 2D threshold. The 3D threshold is currently setat 7 and the 2D threshold is currently set at 5. These thresholds havebeen determined by extensive training on images where the fetal head wasknown to be present or absent.

Thereafter, the fetal head detection algorithm concludes with a fillcircle region 746 that incorporates pixels to the image within thedetected circle. The fill circle region 746 algorithm fills the insideof the best fitting polar circle. Accordingly, the fill circle region746 algorithm encloses and defines the area of the fetal brain tissue,permitting the area and volume to be calculated and deducted viaalgorithm 554 from the apparent amniotic fluid area and volume (AAFA orAAFV) to obtain a computation of the corrected amniotic fluid area orvolume via algorithm 484.

FIG. 10 shows the results of sequentially applying the algorithm stepsof FIGS. 7 and 8A-D on an unprocessed sample image 820 presented withinthe confines of a scanplane substantially equivalent to the scanplane210. The results of applying the heat filter 514 and shock filter 518 inenhancing the unprocessed sample is shown in enhanced image 840. Theresult of intensity-based segmentation algorithms 522 is shown in image850. The results of edge-based segmentation 438 algorithm usingsub-algorithms 526, 530, 534 and 538 of the enhanced image 840 is shownin segmented image 858. The result of the combination 442 utilizing theBoolean AND images 442 algorithm is shown in image 862 where whiterepresents the amniotic fluid area. The result of applying the polishing464 algorithm employing algorithms 542, 546, 550, 554, 560, and 564 isshown in image 864, which depicts the amniotic fluid area overlaid onthe unprocessed sample image 810.

FIG. 11 depicts a series of images showing the results of the abovemethod to automatically detect, locate, and measure the area and volumeof a fetal head using the algorithms outlined in FIGS. 7 and 8A-F.Beginning with an input image in polar coordinate form 920, the fetalhead image is marked by distinctive bright echoes from the anterior andposterior walls of the fetal skull and a circular shape of the fetalhead in the Cartesian image. The fetal head detection algorithm 734operates on the polar coordinate data (i.e., pre-scan version, not yetconverted to Cartesian coordinates).

An example output of applying the head edge detection 734 algorithm todetect potential head edges is shown in image 930. Occupying the spacebetween the anterior and posterior walls are dilated black pixels 932(stacks or short lines of black pixels representing thick edges). Anexample of the polar Hough transform 738 for one actual data sample fora specific radius is shown in polar coordinate image 940.

An example of the best-fit circle on real data polar data is shown inpolar coordinate image 950 that has undergone the find maximum Houghvalue step 742. The polar coordinate image 950 is scan-converted to aCartesian data in image 960 where the effects of finding maximum Houghvalue 742 algorithm are seen in Cartesian format.

FIG. 12 presents a 4-panel series of sonographer amniotic fluid pocketoutlines compared to the algorithm's output in a scanplane equivalent toscanplane 210. The top two panels depict the sonographer's outlines ofamniotic fluid pockets obtained by manual interactions with the displaywhile the bottom two panels show the resulting amniotic fluid boundariesobtained from the instant invention's automatic application of 2Dalgorithms, 3D algorithms, combination heat and shock filter algorithms,and segmentation algorithms.

After the contours on all the images have been delineated, the volume ofthe segmented structure is computed. Two specific techniques for doingso are disclosed in detail in U.S. Pat. No. 5,235,985 to McMorrow et al,herein incorporated by reference. This patent provides detailedexplanations for non-invasively transmitting, receiving and processingultrasound for calculating volumes of anatomical structures.

Multiple Image Cone Acquisition and Image Processing Procedures

In some embodiments, multiple cones of data acquired at multipleanatomical sampling sites may be advantageous. For example, in someinstances, the pregnant uterus may be too large to completely fit in onecone of data sampled from a single measurement or anatomical site of thepatient (patient location). That is, the transceiver 10 is moved todifferent anatomical locations of the patient to obtain different 3Dviews of the uterus from each measurement or transceiver location.

Obtaining multiple 3D views may be especially needed during the thirdtrimester of pregnancy, or when twins or triplets are involved. In suchcases, multiple data cones can be sampled from different anatomicalsites at known intervals and then combined into a composite image mosaicto present a large uterus in one, continuous image. In order to make acomposite image mosaic that is anatomically accurate without duplicatingthe anatomical regions mutually viewed by adjacent data cones,ordinarily it is advantageous to obtain images from adjacent data conesand then register and subsequently fuse them together. In a preferredembodiment, to acquire and process multiple 3D data sets or imagescones, at least two 3D image cones are generally preferred, with oneimage cone defined as fixed, and the other image cone defined as moving.

The 3D image cones obtained from each anatomical site may be in the formof 3D arrays of 2D scanplanes, similar to the 3D array 240. Furthermore,the 3D image cone may be in the form of a wedge or a translational arrayof 2D scanplanes. Alternatively, the 3D image cone obtained from eachanatomical site may be a 3D scancone of 3D-distributed scanlines,similar to the scancone 300.

The term “registration” with reference to digital images means thedetermination of a geometrical transformation or mapping that alignsviewpoint pixels or voxels from one data cone sample of the object (inthis embodiment, the uterus) with viewpoint pixels or voxels fromanother data cone sampled at a different location from the object. Thatis, registration involves mathematically determining and converting thecoordinates of common regions of an object from one viewpoint to thecoordinates of another viewpoint. After registration of at least twodata cones to a common coordinate system, the registered data coneimages are then fused together by combining the two registered dataimages by producing a reoriented version from the view of one of theregistered data cones. That is, for example, a second data cone's viewis merged into a first data cone's view by translating and rotating thepixels of the second data cone's pixels that are common with the pixelsof the first data cone. Knowing how much to translate and rotate thesecond data cone's common pixels or voxels allows the pixels or voxelsin common between both data cones to be superimposed into approximatelythe same x, y, z, spatial coordinates so as to accurately portray theobject being imaged. The more precise and accurate the pixel or voxelrotation and translation, the more precise and accurate is the commonpixel or voxel superimposition or overlap between adjacent image cones.The precise and accurate overlap between the images assures theconstruction of an anatomically correct composite image mosaicsubstantially devoid of duplicated anatomical regions.

To obtain the precise and accurate overlap of common pixels or voxelsbetween the adjacent data cones, it is advantageous to utilize ageometrical transformation that substantially preserves most or alldistances regarding line straightness, surface planarity, and anglesbetween the lines as defined by the image pixels or voxels. That is, thepreferred geometrical transformation that fosters obtaining ananatomically accurate mosaic image is a rigid transformation thatdoesn't permit the distortion or deforming of the geometrical parametersor coordinates between the pixels or voxels common to both image cones.

The preferred rigid transformation first converts the polar coordinatescanplanes from adjacent image cones into in x, y, z Cartesian axes.After converting the scanplanes into the Cartesian system, a rigidtransformation, T, is determined from the scanplanes of adjacent imagecones having pixels in common. The transformation T is a combination ofa three-dimensional translation vector expressed in Cartesian ast=(T_(x), T_(y), T_(z)), and a three-dimensional rotation R matrixexpressed as a function of Euler angles θ_(x), θ_(y), θ_(z) around thex, y, and z axes. The transformation represents a shift and rotationconversion factor that aligns and overlaps common pixels from thescanplanes of the adjacent image cones.

In the preferred embodiment of the present invention, the common pixelsused for the purposes of establishing registration of three-dimensionalimages are the boundaries of the amniotic fluid regions as determined bythe amniotic fluid segmentation algorithm described above.

Several different protocols may be used to collect and process multiplecones of data from more than one measurement site are described in FIGS.13-14.

FIG. 13 illustrates a 4-quadrant supine procedure to acquire multipleimage cones around the center point of uterine quadrants of a patient ina supine procedure. Here the patient lies supine (on her back)displacing most or all of the amniotic fluid towards the top. The uterusis divided into 4 quadrants defined by the umbilicus (the navel) and thelinea-nigra (the vertical center line of the abdomen) and a single 3Dscan is acquired at each quadrant. The 4-quadrant supine protocolacquires four different 3D scans in a two dimensional grid, each cornerof the grid being a quadrant midpoint. Four cones of data are acquiredby the transceiver 10 along the midpoints of quadrant 1, quadrant 2,quadrant 3, and quadrant 4. Thus, one 3D data cone per uterine quadrantmidpoint is acquired such that each quadrant midpoint is mutuallysubstantially equally spaced from each other in a four-corner gridarray.

FIG. 14 illustrates a multiple lateral line procedure to acquiremultiple image cones in a linear array. Here the patent lies laterally(on her side), displacing most or all of the amniotic fluid towards thetop. Four 3D images cones of data are acquired along a line ofsubstantially equally space intervals. As illustrated, the transceiver10 moves along the lateral line at position 1, position 2, position 3,and position 4. As illustrated in FIG. 14, the inter-position distanceor interval is approximately 6 cm.

The preferred embodiment for making a composite image mosaic involvesobtaining four multiple image cones where the transceiver 10 is placedat four measurement sites over the patient in a supine or lateralposition such that at least a portion of the uterus is ultrasonicallyviewable at each measurement site. The first measurement site isoriginally defined as fixed, and the second site is defined as movingand placed at a first known inter-site distance relative to the firstsite. The second site images are registered and fused to the first siteimages After fusing the second site images to the first site images, thethird measurement site is defined as moving and placed at a second knowninter-site distance relative to the fused second site now defined asfixed. The third site images are registered and fused to the second siteimages Similarly, after fusing the third site images to the second siteimages, the fourth measurement site is defined as moving and placed at athird known inter-site distance relative to the fused third site nowdefined as fixed. The fourth site images are registered and fused to thethird site images

The four measurement sites may be along a line or in an array. The arraymay include rectangles, squares, diamond patterns, or other shapes.Preferably, the patient is positioned such that the baby moves downwardwith gravity in the uterus and displaces the amniotic fluid upwardstoward the measuring positions of the transceiver 10.

The interval or distance between each measurement site is approximatelyequal, or may be unequal. For example in the lateral protocol, thesecond site is spaced approximately 6 cm from the first site, the thirdsite is spaced approximately 6 cm from the second site, and the fourthsite is spaced approximately 6 cm from the third site. The spacing forunequal intervals could be, for example, the second site is spacedapproximately 4 cm from the first site, the third site is spacedapproximately 8 cm from the second site, and the third is spacedapproximately 6 cm from the third site. The interval distance betweenmeasurement sites may be varied as long as there are mutually viewableregions of portions of the uterus between adjacent measurement sites.

For uteruses not as large as requiring four measurement sites, two andthree measurement sites may be sufficient for making a composite 3Dimage mosaic. For three measurement sites, a triangular array ispossible, with equal or unequal intervals. Furthermore, is the case whenthe second and third measurement sites have mutually viewable regionsfrom the first measurement site, the second interval may be measuredfrom the first measurement site instead of measuring from the secondmeasurement site.

For very large uteruses not fully captured by four measurement oranatomical sites, greater than four measurement sites may be used tomake a composite 3D image mosaic provided that each measurement site isultrasonically viewable for at least a portion of the uterus. For fivemeasurement sites, a pentagon array is possible, with equal or unequalintervals. Similarly, for six measurement sites, a hexagon array ispossible, with equal or unequal intervals between each measurement site.Other polygonal arrays are possible with increasing numbers ofmeasurement sites.

The geometrical relationship between each image cone must be ascertainedso that overlapping regions can be identified between any two imagecones to permit the combining of adjacent neighboring cones so that asingle 3D mosaic composite image is produced from the 4-quadrant orin-line laterally acquired images.

The translational and rotational adjustments of each moving cone toconform with the voxels common to the stationary image cone is guided byan inputted initial transform that has the expected translational androtational values. The distance separating the transceiver 10 betweenimage cone acquisitions predicts the expected translational androtational values. For example, as shown in FIG. 14, if 6 cm separatesthe image cones, then the expected translational and rotational valuesare proportionally estimated. For example, the (T_(x), T_(y), T_(z)) and(θ_(x), θ_(y), θ_(z)) Cartesian and Euler angle terms fixed images pvoxel values are defined respectively as (6 cm, 0 cm, 0 cm) and (0 deg,0 deg, 0 deg).

FIG. 15 is a block diagram algorithm overview of the registration andcorrecting algorithms used in processing multiple image cone data sets.The algorithm overview 1000 shows how the entire amniotic fluid volumemeasurement process occurs from the multiply acquired image cones.First, each of the input cones 1004 is segmented 1008 to detect allamniotic fluid regions. The segmentation 1008 step is substantiallysimilar to steps 418-480 of FIG. 7. Next, these segmented regions areused to align (register) the different cones into one common coordinatesystem using a Rigid Registration 1012 algorithm. Next, the registereddatasets from each image cone are fused with each other using a FuseData 1016 algorithm to produce a composite 3D mosaic image. Thereafter,the total amniotic fluid volume is computed 1020 from the fused orcomposite 3D mosaic image.

FIG. 16 is a block diagram of the steps of the rigid registrationalgorithm 1012. The rigid algorithm 1012 is a 3D image registrationalgorithm and is a modification of the Iterated Closest Point (ICP)algorithm published by P J Besl and N D McKay, in “A Method forRegistration of 3-D Shapes,” IEEE Trans. Pattern Analysis & MachineIntelligence, vol. 14, no. 2, February 1992, pp. 239-256. The steps ofthe rigid registration algorithm 1012 serves to correct for overlapbetween adjacent 3D scan cones acquired in either the 4-quadrant supinegrid procedure or lateral line multi data cone acquisition procedures.The rigid algorithm 1012 first processes the fixed image 1104 in polarcoordinate terms to Cartesian coordinate terms using the 3D Scan Convert1108 algorithm. Separately, the moving image 1124 is also converted toCartesian coordinates using the 3D Scan Convert 1128 algorithm. Next,the edges of the amniotic fluid regions on the fixed and moving imagesare determined and converted into point sets p and q respectively by a3D edge detection process 1112 and 1132. Also, the fixed image pointset, p, undergoes a 3D distance transform process 1116 which maps everyvoxel in a 3D image to a number representing the distance to the closestedge point in p. Pre-computing this distance transform makes subsequentdistance calculations and closest point determinations very efficient.

Next, the known initial transform 1136, for example, (6, 0, 0) for theCartesian T_(x), T_(y), T_(z) terms and (0, 0, 0) for the θ_(x), θ_(y),θ_(z) Euler angle terms for an inter-transceiver interval of 6 cm, issubsequently applied to the moving image by the Apply Transform 1140step. This transformed image is then compared to the fixed image toexamine for the quantitative occurrence of overlapping voxels. If theoverlap is less than 20%, there are not enough common voxels availablefor registration and the initial transform is considered sufficient forfusing at step 1016.

If the overlapping voxel sets by the initial transform exceed 20% of thefixed image p voxel sets, the q-voxels of the initial transform aresubjected to an iterative sequence of rigid registration.

A transformation T serves to register a first voxel point set p from thefirst image cone by merging or overlapping a second voxel point set qfrom a second image cone that is common to p of the first image cone. Apoint in the first voxel point set p may be defined as p_(i)=(x_(i),y_(i), z_(i)) and a point in the second voxel point set q may similarlybe defined as q_(j)=(x_(j),y_(j), z_(j)), If the first image cone isconsidered to be a fixed landmark, then the T factor is applied to align(translate and rotate) the moving voxel point set q onto the fixed voxelpoint set p.

The precision of T is often affected by noise in the images thataccordingly affects the precision of t and R, and so the variability ofeach voxel point set will in turn affect the overall variability of eachmatrix equation set for each point. The composite variability betweenthe fixed voxel point set p and a corresponding moving voxel point set qis defined to have a cross-covariance matrix C_(pq), more fullydescribed in equation E8 as:

$\begin{matrix}{C_{pq} = {\frac{1}{n}{\sum\limits_{i = {1\mspace{11mu}\ldots\mspace{11mu} n}}{\left( {p_{i} - \overset{\_}{p}} \right)\left( {q_{i} - \overset{\_}{q}} \right)^{T}}}}} & {E8}\end{matrix}$

-   -   where, n is the number of points in each point set and p and q        are the central points in the two voxel point sets. How strong        the correlation is between two sets data is determined by        statistically analyzing the cross-covariance C_(pq). The        preferred embodiment uses a statistical process known as the        Single Value Decomposition (SVD) originally developed by Eckart        and Young (G. Eckart and G. Young, 1936, The Approximation of        One Matrix by Another of Lower Rank, Pychometrika 1, 211-218).        When numerical data is organized into matrix form, the SVD is        applied to the matrix, and the resulting SVD values are        determined to solve for the best fitting rotation transform R to        be applied to the moving voxel point set q to align with the        fixed voxel point set p to acquire optimum overlapping accuracy        of the pixel or voxels common to the fixed and moving images.    -   Equation E9 gives the SVD value of the cross-covariance C_(pq):        C_(pq)=UDV^(t)  E9        -   where D is a 3×3 diagonal matrix and U and V are orthogonal            3×3 matrices    -   Equation E10 further defines the rotational R description of the        transformation T in terms of U and V orthogonal 3×3 matrices as:        R=UV^(T)  E10    -   Equation E11 further defines the translation transform t        description of the transformation T in terms of p, q and R as:        t= p−R q   E11    -   Equations E8 through E11 present a method to determine the rigid        transformation between two point sets p and q—this process        corresponds to step 1152 in FIG. 17.

The steps of the registration algorithm are applied iteratively untilconvergence. The iterative sequence includes a Find Closest Points onFixed Image 1148 step, a Determine New Transform 1152 step, a CalculateDistances 1156 step, and Converged decision 1160 step.

In the Find Closest Points on Fixed Image 1148 step, corresponding qpoints are found for each point in the fixed set p. Correspondence isdefined by determining the closest edge point on q to the edge point ofp. The distance transform image helps locate these closest points. Oncep and closest −q pixels are identified, the Determine New Transform 1152step calculates the rotation R via SVD analysis using equations E8-E10and translation transform t via equation E11. If, at decision step 1160,the change in the average closest point distance between two iterationsis less than 5%, then the predicted-q pixel candidates are consideredconverged and suitable for receiving the transforms R and t to rigidlyregister the moving image Transform 1136 onto the common voxels p of the3D Scan Converted 1108 image. At this point, the rigid registrationprocess is complete as closest proximity between voxel or pixel sets hasoccurred between the fixed and moving images, and the process continueswith fusion at step 1016.

If, however, there is >5% change between the predicted-q pixels and ppixels, another iteration cycle is applied via the Apply Transform 1140to the Find Closest Points on Fixed Image 1148 step, and is cycledthrough the converged 1160 decision block. Usually in 3 cycles, thoughas many as 20 iterative cycles, are engaged until is the transformationT is considered converged.

A representative example for the application of the preferred embodimentfor the registration and fusion of a moving image onto a fixed image isshown in FIGS. 17A-17C.

FIG. 17A is a first measurement view of a fixed scanplane 1200A from a3D data set measurement taken at a first site. A first pixel set pconsistent for the dark pixels of AFV is shown in a region 1204A. Theregion 1204A has approximate x-y coordinates of (150, 120) that isclosest to dark edge.

FIG. 17B is a second measurement view of a moving scanplane 1200B from a3D data set measurement taken at a second site. A second pixel set qconsistent for the dark pixels of AFV is shown in a region 1204B. Theregion 1204B has approximate x-y coordinates of (50, 125) that isclosest to dark edge.

FIG. 17C is a composite image 1200C of the first (fixed) 1200A andsecond (moving) 1200B images in which common pixels 1204B at approximatecoordinates (50, 125) is aligned or overlapped with the voxels 1204A atapproximate coordinates (150, 120). That is, the region 1204B pixel setq is linearly and rotational transformed consistent with the closestedge selection methodology as shown in FIGS. 13A and 13B from employingthe 3D Edge Detection 1112 step. The composite image 1200C is a mosaicimage from scanplanes having approximately the same φ and rotation θangles.

The registration and fusing of common pixel sets p and q from scanplaneshaving approximately the same φ and rotation θ angles can be repeatedfor other scanplanes in each 3D data set taken at the first (fixed) andsecond(moving) anatomical sites. For example, if the composite image1200C above was for scanplane #1, then the process may be repeated forthe remaining scanplanes #2-24 or #2-48 or greater as needed to capturea completed uterine mosaic image. Thus an array similar to the 3D array240 from FIG. 5B is assembled, except this time the scanplane array ismade of composite images, each composited image belonging to a scanplanehaving approximately the same φ and rotation θ angles.

If a third and a fourth 3D data sets are taken, the respectiveregistration, fusing, and assembling into scanplane arrays of compositedimages is undertaken with the same procedures. In this case, thescanplane composite array similar to the 3D array 240 is composed of agreater mosaic number of registered and fused scanplane images.

A representative example the fusing of two moving images onto a fixedimage is shown in FIGS. 18A-18D.

FIG. 18A is a first view of a fixed scanplane 1220A. Region 1224A isidentified asp voxels approximately at the coordinates (150, 70).

FIG. 18B is a second view of a first moving scanplane 1220B having someq voxels 1224B at x-y coordinates (300, 100) common with the firstmeasurements p voxels at x-y coordinates (150, 70). Another set ofvoxels 1234A is shown roughly near the intersection of x-y coordinates(200, 125). As the transceiver 10 was moved only translationally, Thescanplane 1220B from the second site has approximately the same tilt φand rotation θ angles of the fixed scanplane 1220A taken from the firstlateral in-line site.

FIG. 18C is a third view of a moving scanplane 1220C. A region 1234B isidentified as q voxels approximately at the x-y coordinates (250, 100)that are common with the second views q voxels 1234A. The scanplane 1220c from the third lateral in-line site has approximately the same tilt φand rotation θ angles of the fixed scanplane 1220A taken from the firstlateral in-line site and the first moving scanplane 1220B taken from thesecond lateral in-line site.

FIG. 18D is a composite mosaic image 1220D of the first (fixed) 1220Aimage, the second (moving) 1220B image, and the third (moving) 1220Cimage representing the sequential alignment and fusing of q voxel sets1224B to 1224A, and 1234B with 1234A.

A fourth image similarly could be made to bring about a 4-image mosaicfrom scanplanes from a fourth 3D data set acquired from the transceiver10 taking measurements at a fourth anatomical site where the fourth 3Ddata set is acquired with approximately the same tilt φ and rotation θangles.

The transceiver 10 is moved to different anatomical sites to collect 3Ddata sets by hand placement by an operator. Such hand placement couldcreate the acquiring of 3D data sets under conditions in which the tiltφ and rotation θ angles are not approximately equal, but differ enoughto cause some measurement error requiring correction to use the rigidregistration 1012 algorithm. In the event where the 3D data sets betweenanatomical sites, either between a moving supine site in relation to itsbeginning fixed site, or between a moving lateral site with itsbeginning fixed site, cannot be acquired with the tilt φ and rotation θangles being approximately the same, then the built-in accelerometermeasures the changes in tilt φ and rotation θ angles and compensatesaccordingly so that acquired moving images are presented if though theywere acquired under approximately equal tilt φ and rotation θ angleconditions.

FIG. 19 illustrates a 6-section supine procedure to acquire multipleimage cones around the center point of a uterus of a patient in a supineposition. Each of the 6 segments are scanned in the order indicated,starting with segment 1 on the lower right side of the patient. Thedisplay on the scanner 10 is configured to indicate how many segmentshave been scanned, so that the display shows “0 of 6,” “1 of 6,” . . .“6 of 6.” The scans are positioned such that the lateral distancesbetween each scanning position (except between positions 3 and 4) areapproximately about 8 cm. Alternate intervals are used in alternateembodiments.

To repeat the scan, the top button of the scanner 10 is repetitivelydepressed, so that it returns the scan to “0 of 6,” to permit a user torepeat all six scans again. Finally, the scanner 10 is returned to thecradle to upload the raw ultrasound data to computer, intranet, orInternet as depicted in FIGS. 2C, 3, and 4 for algorithmic processing,as will be described in detail below. Within a time period, preferablypredetermined, a result is generated that includes an estimate of theamniotic fluid volume.

As with the quadrant and the four in-line scancone measuring methodsdescribed earlier, the six-segment procedure ensures that themeasurement process detects all amniotic fluid regions. The transceiver10 projects outgoing ultrasound signals, in this case into the uterineregion of a patient, at six anatomical locations, and receives incomingechoes reflected back from the regions of interest to the transceiver 10positioned at a given anatomical location. An array of scanplane imagesis obtained for each anatomical location based upon the incoming echosignals. Image enhanced and segmented regions for the scanplane imagesare determined, preferably for each scanplane array, which may be arotational, wedge, or translationally configured scanplane array. Thesegmented regions are used to align or register the different scanconesinto one common coordinate system. Thereafter, the registered datasetsare merged with each other so that the total amniotic fluid volume iscomputed from the resulting fused image.

FIG. 20 is a block diagrammatic overview of an algorithm for theregistration and correction processing of the 6-section multiple imagecone data sets depicted in FIG. 19. A six-section algorithm overview1000A includes many of the same blocks of algorithm overview 1000depicted in FIG. 15. However, the segmentation registration proceduresare modified for the 6-section multiple image cones. In the algorithmoverview 1000A, the sub-processes include the InputCones block 1004, anImage Enhancement and Segmentation block 1010, a RigidRegistration block1014, the FuseData block 1016, and the CalculateVolume block 1020.Generally, the Image Enhanced and Segmentation block 1010 reduces theeffects of “noise”, which may include speckle noise, in the data whilepreserving the salient edges on the image. The enhanced images are thensegmented by an edge-based and intensity-based method, and the resultsof each segmentation method are then subsequently combined. The resultsof the combined segmentation method are then cleaned up to fill gaps andto remove outliers. The area and/or the volume of the segmented regionsare then computed.

FIG. 21 is a more detailed view of the Image Enhancement andSegmentation block 1010 of FIG. 20. Very similar to the algorithmprocesses of Image Enhancement 418, Intensity-based segmentation 422,and Edge-based segmentation 438 explained for FIG. 7, theenhancement-segmentation block 1010 begins with an input data block1010A2, wherein the signals of pixel image data are subjected to ablurring and speckle removal process followed by a sharpening orde-blurring process. The combination of blurring and speckle removalfollowed by sharpening or de-blurring enhances the appearance of thepixel-based input image.

The blurring and de-blurring is achieved by a combination of heat andshock filters. The inputed pixel related data from process 1010A2 isfirst subjected to a heat filter process block 1010A4. The heat filterblock 1010A4 is a Laplacian-based filtering process and results inreduction of the speckle noise and smooths or otherwise blurs the edgesin the image. The heat filter block 1010A4 is modified via auser-determined stored data block 1010A6 wherein the number of heatfilter iterations and step sizes are defined by the user and are appliedto the inputed data 1010A2 in the heat filter process block 1010A4. Theeffect of heat iteration number in progressively blurring and removingspeckle from an original image as the number of iteration cycles isincreased is shown in FIG. 23. Once the pixel image data has been heatfilter processed, the pixel image data is further processed by a shockfilter block 1010A8. The shock filter block 1010A8 is subjected to auser-determined stored data block 1010A10 wherein the number shockfilter iterations, step sizes, and gradient threshold are specified bythe user. The foregoing values are then applied to heat filtered pixeldata in the shock filter block 1010A8. The effect of shock iterationnumber, step sizes, and gradient thresholds in reducing the blurring isseen in signal plots (a) and (b) of FIG. 24. Thereafter, a heat andshock-filtered pixel data is parallel processed in two algorithmpathways, as defined by blocks 1010B2-6 (Intensity-Based SegmentationGroup) and blocks 1010C2-4 (Edge-Based Segmentation Group).

The Intensity-based Segmentation utilizes the observation that amnioticfluid is usually darker than the rest of the image. Pixels associatedwith fluids are classified based upon a threshold intensity level. Thuspixels below this intensity threshold level are interpreted as fluid,and pixels above this intensity threshold are interpreted as solid ornon-fluid tissues. However, pixel values within a dataset can varywidely, so a means to automatically determine a threshold level within agiven dataset is required in order to distinguish between fluid andnon-fluid pixels. The intensity-based segmentation is divided into threesteps. A first step includes estimating the fetal body and shadowregions, a second step includes determining an automatic thresholdingfor the fluid region after removing the body region, and a third stepincludes removing the shadow and fetal body regions from the potentialfluid regions. In alternate embodiments, different sequences of thesesteps, and more or fewer steps are utilized.

The Intensity-Based Segmentation Group includes a fetal body regionblock 1010B2, wherein an estimate of the fetal shadow and body regionsis obtained. Generally, the fetal body regions in ultrasound imagesappear bright and are relatively easily detected. Commonly, anteriorbright regions typically correspond with the dome reverberation of thetransceiver 10, and the darker appearing uterus is discernable againstthe bright pixel regions formed by the more echogenic fetal body thatcommonly appears posterior to the amniotic fluid region. In fetal bodyregion block 1010B2, the fetal body and shadow is found in scanlinesthat extend between the bright dome reverberation region and theposterior bright-appearing fetal body. A magnitude of the estimate offetal and body region is then modified by a user-determined inputparameter stored in a body threshold data block 1010B4, and a pixelvalue is chosen by the user. For example, a pixel value of 40 may beselected by the user. An example of the image obtained from blocks1010B2-4 is panel (c) of FIG. 25. Once the fetal body regions and theshadow has been estimated, an automatic region threshold block 1010B6 isapplied to this estimate to determine which pixels are fluid related andwhich pixels are non-fluid related. The automatic region threshold block1010B6 uses a version of the Otsu algorithm (R M Haralick and L GShaprio, Computer and Robot Vision, vol. 1, Addison Wesley 1992, page11, incorporated by reference). Briefly, and in general terms, the Otsualgorithm determines a threshold value from an assumed bimodal pixelvalue histogram that generally corresponds to fluid and some soft tissue(non-fluid) such as placental or other fetal or maternal soft tissue.All pixel values less than the threshold value as determined by the Otsualgorithm are designated as potential fluid pixels. Using the Otsualgorithm determined threshold value, the first pathway is completed bya removing body regions above this threshold value in block 1010B8 sothat the amniotic fluid regions are isolated. An example of the effectof the Intensity-based segmentation group is shown in panel (d) of FIG.25. The isolated amniotic fluid region image thus obtained from theintensity-based segmentation process is then processed for subsequentcombination with the end result of the second edge-based segmentationmethod.

Referring now to the second pathway or the Edge-Based SegmentationGroup, the procedural blocks find pixel points on an image having highspatial gradient magnitudes. The edge-based segmentation process beginsprocessing the shock filtered 1010A8 pixel data via a spatial gradientsblock 1010C2 in which the gradient magnitude of a given pixelneighborhood within the image is determined. The gradient magnitude isdetermined by the taking the X and Y derivatives using the differencekernels shown in FIG. 26.

The gradient magnitude of the image is given by Equation E7:∥I∥=√{square root over (I _(x) ² +I _(y) ²)}I _(x) =I*K _(x)I _(y) =I*K _(y)  E7

-   -   where * is the convolution operator.

Once the gradient magnitude is determined, pixel edge points aredetermined by a hysteresis threshold of gradients process block 1010C4.In block 1010C4, a lower and upper threshold value is selected. Theimage is then thresholded using the lower value and a connectedcomponent labeling is performed on the resulting image. The pixel valueof each connected component is measured to determine which pixel edgepoints have gradient magnitude pixel values equal to or greater than theupper threshold value. Those pixel edge points having gradient magnitudepixel values equal to or exceeding the upper threshold are retained.This retention of pixels having strong gradient values serves to retainselected long connected edges which have one or more high gradientpoints.

Thereafter, the image is thresholded using the upper value, and aconnected component labeling is carried out on the resulting image. Thehysteresis threshold 1010C4 is modified by a user-determined edgethreshold block 1010C6. An example of an application of the secondpathway will be shown in panels (b) for the spatial gradients block1010C2 and (c) for the threshold of gradients process block 1010C4 ofFIG. 27. Another example of application of the edge detection blockgroup for blocks 1010C2 and 1010C4 can also be seen in panel (e) of FIG.25.

Referring again to FIG. 21, the first and second pathways are merged ata combined region and edges process block 1010D2. The combining processavoids erroneous segmentation arising from either the intensity-based oredge-based segmentation processes. The goal of the combining process isto ensure that good edges are reliably identified so that fluid regionsare bounded by strong edges. Intensity-based segmentation mayunderestimate fluid volume, so that the boundaries need to be correctedusing the edge-based segmentation information. In block 1010D2, thebeginning and end of each scanline within the segmented region isdetermined by searching for edge pixels on each scanline. If no edgepixels are found in the search region, the segmentation on that scanlineis removed. If edge pixels are found, then the region boundary locationsare moved to the location of these edge pixels. Panel (f) of FIG. 25illustrates the effects of the combining block 1010D2.

The segmentation resulting from the combination of region and edgeinformation occasionally includes extraneous regions or even holes. Acleanup stage helps ensure consistency of segmented regions in a singlescanplane and between scanplanes. The cleanup stage uses morphologicaloperators (such as erosion, dilation, opening, closing) using the MarkovRandom Fields (MRFs) as disclosed in Forbes et al. (Florence Forbes andAdrian E. Raftery, “Bayesian morphology: Fast Unsupervised BayesianImage Analysis,” Journal of the American Statistical Association, June1999, herein incorporated by reference). The combined segmentationimages receive the MRFs by being subjected to an In-plane Closing andOpening process block 1010D4. The In-plane opening-closing block 1010D4block is a morphological operator wherein pixel regions are opened toremove pixel outliers from the segmented region, or that fills in or“closes” gaps and holes in the segmented region within a givenscanplane. Block 1010D4 uses a one-dimensional structuring elementextending through five scanlines. The closing-opening block is affectedby a user-determined width, height, and depth parameter block 1010D6.Thereafter, an Out-of-plane Closing and Opening processing block 1010D8is applied. The block 1010D8 applies a set of out-of-plane morphologicalclosings and openings using a one-dimensional structuring elementextending through three scanlines. Pixel inconsistencies are accordinglyremoved between the scanplanes. Panel (g) of FIG. 25 illustrates theeffects of the blocks 1010D4-8.

FIG. 22 is an expansion of the RigidRegistration block 1014 of FIG. 20.Similar in purpose and general operation using the previously describedICP algorithm as used in the RigidRegistration block 1012 of FIG. 16,the block 1014 begins with parallel inputs of a fixed Image 1014A, aMoving Image 1014B, and an Initial Transform input 1014B10.

The steps of the rigid registration algorithm 1014 correct any overlapsbetween adjacent 3D scan cones acquired in the 6-section supine gridprocedure. The rigid algorithm 1014 first converts the fixed image1104A2 from polar coordinate terms to Cartesian coordinate terms usingthe 3D Scan Convert 1014A4 algorithm. Separately, the moving image1014B2 is also converted to Cartesian coordinates using the 3D ScanConvert 1014B4 algorithm. Next, the edges of the amniotic fluid regionson the fixed and moving images are determined and converted into pointsets p and q, respectively by a 3D edge detection process 1014A6 and1014B6. Also, the fixed image point set, p, undergoes a 3D distancetransform process 1014B8 which maps every voxel in a 3D image to anumber representing the distance to the closest edge point in p.Pre-computing this distance transform makes subsequent distancecalculations and closest point determinations very efficient.

Next, the known initial transform 1014B10, for example, (6, 0, 0) forthe Cartesian T_(x), T_(y), T_(z) terms and (0, 0, 0) for the θ_(x),θ_(y), θ_(z) Euler angle terms, for an inter-transceiver interval of 6cm, is subsequently applied to the moving image by the transform edges1014B8 block. This transformed image is then subjected to the FindClosest Points on Fixed Image block 1014C2, similar in operation to theblock 1148 of FIG. 16. Thereafter, a new transform is determined inblock 1014C4, and the new transform is queried for convergence atdecision diamond 1014C8. If conversion is attained, theRigidRegistration 1014 is complete at terminus 1014C10. Alternatively,if conversion is not attained, then a return to the transform edgesblock 1014B8 occurs to start another iterative cycle.

The RigidRegistration block 1014 typically converges in less than 20iterations. After applying the initial transformation, the entireregistration process is performed in case there are any overlappingsegmented regions between any two images. Similar to the processdescribed in connection with FIG. 16, an overlap threshold ofapproximately 20% is currently set as an input parameter.

FIG. 23 is a 4-panel image set that shows the effect of multipleiterations of the heat filter applied to an original image. The effectof shock iteration number in progressively blurring and removing specklefrom an original image as the number of iterations increases is shown inFIG. 23. In this case the heat filter is described by process blocks1010A4 and A6 of FIG. 21. In this example, an original image of abladder is shown in panel (a) having visible speckle spread throughoutthe image. Some blurring is seen with the 10 iteration image in panel(b), followed by more progressive blurring at 50 iterations in panel (c)and 100 iterations in panel (d). As the blurring increases withiteration number, the speckle progressively decreases.

FIG. 24 shows the effect of shock filtering and a combinationheat-and-shock filtering to the pixel values of the image. The effect ofshock iteration number, step sizes, and gradient thresholds on theblurring of a heat filter is seen in ultrasound signal plots (a) and (b)of FIG. 24. Signal plot (a) depicts a smoothed or blurred signalgradient as a sigmoidal long dashed line that is subsequently shockfiltered. As can be seen by the more abrupt or steep stepped signalplots after shock filtering, the magnitude of the shock filtered signal(short dashed line) approaches that of the original signal (solid line)without the choppy or noisy pattern associated with speckle. For themost part there is virtually a complete overlap of the shock filteredsignal with the original signal through the pixel plot range.

Similarly, ultrasound signal plot (b) depicts the effects of applying ashock filter to a noisy (speckle rich) signal line (sinuous long dashline) that has been smooth or blurred by the heat filter (short dashedline with sigmoidal appearance). In operation the shock filter resultsin a generally de-blurring or sharpening of the edges of the image thatwere previously blurred. Adjacent with, but not entirely overlappingwith the original signal (solid line) throughout the pixel plot range,the shock filtered plot substantially overlaps the vertical portion ofthe original signal, but is elevated in the low and high pixel ranges.Like in (a), a more abrupt or steep stepped signal plot after shockfiltering is obtained without significant removal of speckle. Generallydependent on the gradient threshold, step size, and iteration numberimposed by block 1010A10 upon shock block 1010A8, different overlappinglevels of the shock filtered line to that of the original are obtained.

FIG. 25 is a 7-panel image set generated by the image enhancement andsegmentation algorithms of FIG. 21. Panel (a) is an image of theoriginal uterine image. Panel (b) is the image that is produced from theimage enhancement processes primarily described in blocks 1010A4-6 (heatfilters) and blocks 1010A8-10 (shock filters) of FIG. 21. Panel (c)shows the effects of the processing obtained from blocks 1010B2-4(Estimate Shadow and Fetal Body Regions/Body Threshold). Panel (d) isthe image when processed by the Intensity-Based Segmentation Block Group1010B2-8. Panel (e) results from application of the Edge-BasedSegmentation Block Group 1010C2-6. Thereafter, the two Intensity-basedand Edge-based block groups are combined (combining block 1010D2) toresult in the image shown in panel (f). Panel (g) illustrates theeffects of the blocks In-plane and Out-of-plane opening and closingprocessing blocks 1010D4-8.

FIG. 26 is a pixel difference kernel for obtaining X and Y derivativesto determine pixel gradient magnitudes for edge-based segmentation. Asillustrated, a simplest case convolution is obtained for a firstderivative computation where K_(x) and K_(y) are convolution constants.

FIG. 27 is a 3-panel image set showing the progressive demarcation oredge detection of organ wall interfaces arising from edge-basedsegmentation algorithms. Panel (a) is the enhanced input image. Panel(b) is the image result when the enhanced input image is subjected tothe spatial gradients block 1010C2. Panel (c) is the image result whenthe enhanced and spatial gradients 1010C2 processed image is furtherprocessed by the threshold of gradients process block 1010C4.

Demonstrations of the algorithmic manipulation of pixels of the presentinvention are provided in Appendix 1: Examples of Algorithmic Steps.Source code of the algorithms of the present invention is provided inAppendix 2: Matlab Source Code.

While the preferred embodiment of the invention has been illustrated anddescribed, as noted above, many changes can be made without departingfrom the spirit and scope of the invention. For example, other uses ofthe invention include determining the areas and volumes of the prostate,heart, bladder, and other organs and body regions of clinical interest.Similarly, while the preferred sequence of steps and sub-steps (e.g.,algorithms) have been described, alternate sequences and additional orfewer steps can be utilized in alternate embodiments. Accordingly, thescope of the invention is not limited by the disclosure of the preferredembodiment.

1. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second portion of the uterus to obtain a second plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first plurality with the second plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.
 2. The method of claim 1, wherein plurality of scanplanes are acquired from a rotational array, a translational array, or a wedge array.
 3. The method of claim 1, wherein the plurality of algorithms includes algorithms for image enhancement, segmentation, and polishing.
 4. The method of claim 3, wherein segmentation further includes an intensity clustering step, a spatial gradients step, a hysteresis threshold step, a Region-of-Interest selection step, and a matching edges filter step.
 5. The method of claim 4, wherein the intensity clustering step is performed in a first parallel operation, and the spatial gradients, hysteresis threshold, Region-of-Interest selection, and matching edges filter steps are performed in a second parallel operation, and further wherein the results from the first parallel operation are combined with the results from the second parallel operation.
 6. The method of claim 3, wherein image enhancement further includes applying a heat filter and a shock filter to the digital images.
 7. The method of claim 6 wherein the heat filter is applied to the digital images followed by application of the shock filter to the digital images.
 8. The method of claim 1, wherein the amniotic fluid volume is adjusted for underestimation or overestimation.
 9. The method of claim 8, wherein the amniotic fluid volume is adjusted for underestimation by probing with adjustable ultrasound frequencies to penetrate deep tissues and to repositioning the transceiver to establish that deep tissues are exposed with probing ultrasound of sufficient strength to provide a reflecting ultrasound echo receivable by the transceiver, such that more than one rotational array to detect deep tissue and regions of the fetal head are obtained.
 10. The method of claim 8, wherein amniotic fluid volume is adjusted for overestimation by automatically determining fetal head volume contribution to amniotic fluid volume and deducting it from the amniotic fluid volume.
 11. The method of claim 10, wherein the steps to adjust for overestimated amniotic fluid volumes include a 2D clustering step, a matching edges step, an all edges step, a gestational age factor step, a head diameter step, an head edge detection step, and a Hough transform step.
 12. The method of claim 11, wherein the Hough transform step includes a polar Hough Transform step, a Find Maximum Hough value step, and a fill circle region step.
 13. The method of claim 12, wherein the polar Hough Transform step includes a first Hough transform to look for lines of a specified shape, and a second Hough transform to look for fetal head structures.
 14. The method of claim 1, wherein the positions include lateral and transverse.
 15. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second and a third portion of the uterus to obtain a second and third plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first plurality through the third plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.
 16. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second through fourth portion of the uterus to obtain a second through fourth plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first through fourth plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.
 17. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second through fifth portion of the uterus to obtain a second through fifth plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first through the fifth plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.
 18. A system for determining amniotic fluid volume, the system comprising: a transceiver positioned from two to six locations of a patient, the transceiver configured to deliver radio frequency ultrasound pulses to amniotic fluid regions of a patient, to receive echoes of the pulses reflected from the amniotic fluid regions, to convert the echoes to digital form, and to obtain a plurality of scanplanes in the form of an array for each location; a computer system in communication with the transceiver, the computer system having a microprocessor and a memory, the memory further containing stored programming instructions operable by the microprocessor to associate the plurality of scanplanes of each array, and the memory further containing instructions operable by the microprocessor to determine the presence of an amniotic fluid region in each array and determine the amniotic fluid volume in each array.
 19. The system of claim 18, wherein the array includes rotational, wedge, and translation.
 20. The system of claim 18, wherein stored programming instructions further include aligning scanplanes having overlapping regions from each location into a plurality of registered composite scanplanes.
 21. The system of claim 20, wherein the stored programming instructions further include fusing the registered composite scanplanes amniotic fluid regions of the scanplanes of each array.
 22. The system of claim 21 wherein the stored programming instructions further include arranging the fused composite scanplanes into a composite array.
 23. The system of claim 18, wherein the computer system is configured for remote operation via a local area network or an Internet web-based system, the internet web-based system having a plurality of programs that collect, analyze, and store amniotic fluid volume. 